interval: equal distances but NO absolute zero point (complete absence of attribute), ex temp, IQ
ratio: same as ordinal but with absolute zero, can mult & divide

negatively skewed distribution

"easy test"
few scores fall at low end (tail on left/negative end w/lump on right)

positively skewed distribution

"hard test"
few scores fall at high end (tail on right/positive end w/lump on left)

mean, median, mode

mean: average, preferred measure of central tendency, sensitive to extremes-"pulled toward tail"
median: Md, middle value when ordered from low to high, not as effected by extreme scores, useful for skewed distributions
Mode: most frequent value, can be more than one (multimodal, bimodal)

variance

-measure of variability of disribution
-average of squared differences of each score from mean
-equal to the square of the standard deviation (s2)

standard deviation

-square root of variance (s)
-expected deviation from mean of a score chosen at random

z-score (standard score)

-how many standard deviations a given raw score is from the mean
-z-score distributions have sd of 1 and mean of 0

which of post-hoc tests is appropriate if only conducting pairwise comparisons?

-Tukey
-provides enough protection against TI if only pairwise comp are made

main effects vs. interaction effects

-advantage of Factoral ANOVAs
-Main: effect of one IV by itself
-Interaction: effects of an IV at different levels of other IVs

marginal vs. cell means

-Factorial ANOVA
-examine main effects by examining difference in marginal means
-examine interaction effects by examining differences in cell means (if move in opposite directions (cross) across both levels of IV then have interaction effect, if move in same direction i.e. both increase or both decrease (parallel) than no interaction effect

what is caution in interpretation when you find interaction effects?

-must interpret the main effects with caution, main effects don't generalize to all levels of other IVs

name 3 cautions in using Chi-square

1. all observations must be independent
2. each observation can only be classified into one category/cell - must be mutually exclusive
3. %s of observations w/i categories cannot be compared (must convert to #'s)

-squared correlation coefficient
-indicates the percentage of variability in one measure accounted for by variability in other measure
-if IQ & GPA correlate at .70 than 49% of variation in GPA can be explained by variation in IQ (rest is explained by unmeasured factors)

point-biserial and biserial coefficients

-point-biserial: relates one continuous var and one dichotomous var (gender)
-biserial: two continuous var are correlated but one is artifically made dichotomous (high/low)

Phi and Tetrachoric coefficients

-Phi: when both variables are dichotomous
-Tetrachoric: both variables artifically dichotomized

-correlate two variables ordinally ranked (compare two judges rankings on same set of observations)

eta

-measures nonlinear relationships

regression

when two variables are correlated, allows you to estimate the value of one variable based on value of other

predictor vs. criterion in regression equation

predictor is value given and criterion is the predictee or value you are determining

regression analysis can be used as a substitute for what?

one-way ANOVA

multiple correlation coefficient (Multiple R)

-assesses relationship between two+ predictor var and ONE criterion var

multiple regression

use of scores on more than one predictor to estimate scores on a criterion

the multiple correlation coefficient is has highest predictive power when?

predictor variables are highly correlated with criterion but not each other (multi-collinearity)

multiple correlation coefficient is never lower than what?

-the highest simple correlation bt an ind predictor and the criterion

The multiple correlation coefficent can never be what?

negative

coefficient of multiple determination

R squared
-like the pearson r, this notes the proportion of variance in the criterion variable accounted for by the combo of predictor variables

stepwise multiple regression

-forward and backward
-with each addition of predictor variable determine if predictive power of multiple R has increased

canonical correlation

-used with multiple criterion and multiple predictor variables

discriminant function analysis

-used to predict criterion group membership, not a criterion score (like multiple regression)

differential validity

-when each predictor has different correlation with each criterion variable

logistic regression

-used when required assumptions for discriminant analysis are not met (ex. normal dist, homogeneity)
-predictors can be nominal
-primarily used w/dichotomous DVs or when subj can be classified into one of two criterion groups

multiple cutoff

identifying different cutoff scores on a series of predictors, must score at or above the cutoff on EACH predictor to be predicted as successful on criterion

partial correlation

statistically taking out or "partialling out" effect of a variable to control its effect on correlation